import os

# BASE_PATH = '/home/hanpengcheng/projects/changeDetection_pro/SceneChangeDet/code'
# PRETRAIN_MODEL_PATH = os.path.join(BASE_PATH,'pretrain')
DATA_PATH = '../flaskPredict/data/train_data'
TRAIN_DATA_PATH = os.path.join(DATA_PATH,'dataset')
TRAIN_LABEL_PATH = os.path.join(DATA_PATH,'dataset')
TRAIN_TXT_PATH = os.path.join(DATA_PATH,'flask_train.txt')
VAL_DATA_PATH = os.path.join(DATA_PATH,'flask_train.txt')
VAL_LABEL_PATH = os.path.join(DATA_PATH,'flask_train.txt')
VAL_TXT_PATH = os.path.join(DATA_PATH,'flask_train.txt')
TEST_DATA_PATH = os.path.join(DATA_PATH,'dataset')
TEST_TXT_PATH = os.path.join(DATA_PATH,'flask_train.txt')
# SAVE_PATH = '../save_path/flaskPredict'
SAVE_PATH = '../flaskPredict'
if not os.path.exists(SAVE_PATH):
    os.mkdir(SAVE_PATH)
SAVE_CKPT_PATH = os.path.join(SAVE_PATH,'ckpt')
if not os.path.exists(SAVE_CKPT_PATH):
    os.makedirs(SAVE_CKPT_PATH)
SAVE_PRED_PATH = os.path.join(SAVE_PATH,'prediction')
if not os.path.exists(SAVE_PRED_PATH):
    os.makedirs(SAVE_PRED_PATH)

# SIAMESE_BEST_PREFORMANCE_CKPT = '/home/lsc/project/new_changeDetecion_with_little_scale_fuse/save_path/baseline/ckpt/model_best.pth'
# FUSENET_SECOND_CKPT = '/home/lsc/project/new_changeDetecion_with_little_scale_fuse/save_path/flaskPredict/ckpt/final/model_best.pth'

TRAINED_BEST_PERFORMANCE_CKPT = os.path.join(SAVE_CKPT_PATH,'model_best.pth')
print("TRAINED_BEST_PERFORMANCE_CKPT:",TRAINED_BEST_PERFORMANCE_CKPT)
INIT_LEARNING_RATE = 1e-4
DECAY = 5e-5
MOMENTUM = 0.90
MAX_ITER = 40000
BATCH_SIZE = 1
THRESH = 0.5
THRESHS = [0.1,0.3,0.5]
LOSS_PARAM_CONV = 3
LOSS_PARAM_FC = 3
# TRANSFROM_SCALES= (1024,1024)
TRANSFROM_SCALES= (512,512)
T0_MEAN_VALUE = (107.800,117.692,119.979)
T1_MEAN_VALUE = (110.655,117.107,119.135)
    

